Gigaom AI

AI Startups: If You Say You Are Doing AI, Show It

Artificial intelligence is generating a lot of deserved attention for ushering in wide-ranging changes not only in tech but in many aspects of life. Like the Internet, AI is poised to change the way we live our lives and how we work. As with any major disruptive force AI also presents a high signal-to-noise ratio.

AI has now become a buzzword. Startups work AI into their pitches even if their businesses aren’t really oriented around the technology. That’s understandable. It is an exciting time for this technology as we see consumer and B2B acceptance combining with rapid advances in functionality. And even though big tech firms like Google and Facebook get a lot of the attention, I believe it is the startups that will drive the AI disruption wave.

Coming off Gigaom’s GAIN AI startup challenge, I do however see startups struggling to make their case. How do you rise above the noise when making your case to a VC like me? Here are a few questions I’d pose to any AI startup, bearing in mind that there are no typical VCs and no typical startups:

What are the founders passionate about? No matter how dazzling the technology, the human factor is still more important. You can learn a lot about the founders’ grit and business acumen by looking at their track record – but it’s also important to get a sense of their vision and passions. Since AI has far-reaching implications, founders need to be able to see how their businesses will harness data in new ways and take advantage of a pervasive computing environment. They need to have a strong vision of this future and be able to communicate the excitement of that vision.

Are you solving a real-world problem? As a VC, I often come across companies that have developed innovative technology in search of a problem. It’s much better if you have created a solution to an existing and acknowledged problem.

Is AI core to your strategy? If you put AI, machine learning, natural language processing or speech recognition in your deck, you must be able to speak to that. Often when I ask about the inclusion of these terms in a startup’s deck, the founder tells me to speak to the company’s CIO or “data person.” If AI is core to the company’s strategy, it is critical that the CEO needs to know how it works and can speak to it.

What market are you going after? This is all-important. You could have a great solution to a business problem, but if your potential customers are from non-profits, then you aren’t going to make a lot of money. Conversely, if you’re going into a huge and competitive market, how are you differentiating yourself? How will you address that challenge?

Where do your AI people sit in the organization? Google, Amazon, Facebook, Apple, and Baidu are monopolizing AI talent. So, while there is still plenty of opportunity to go after AI talent, the odds are that the majority of the people in your company will not be AI experts. It is important to communicate who exactly has the AI expertise and where they sit within the organization. Are they on the product team or the development team? While there’s no right answer necessarily, I look for companies that can show they’ve thought this through, and can tell me who has the influence and is driving their roadmap.

How talented is your AI talent? Prior to 2006, deep learning didn’t exist, so chances are that your AI expert will be on the young-ish side. Still, if your only AI person is 22 and just out of school, that may not convey your bench strength. Building your team requires thoughtfulness – make sure you are allocating the resources you need to ensure you will succeed.

The bottom line: VCs who know the segment will quickly be able to see, or see-through, the depth of your AI knowledge. To ensure your credibility and be taken seriously as an artificial intelligence startup, if you say you’re doing AI, show it.

Rudina Seseri is founder and managing partner at Glasswing Ventures and Entrepreneur-In-Residence at Harvard Business School.

Rudina — Performing analytics on a data set is one thing. Taking the gee whiz insight and getting someone to change their human behavior is waaaaayy harder than getting the insight. People don’t like to change and they need a retail UX. In the DMAIC methodology, I is improve, and C is control. Analyze is upstream. This is what’s missing in most AI applications — the translation to systematic action through an intuitive operational system, especially when the change is for people and the process is at scale.

Thanks for the article, Rudina. I’d love to know more about types of AI startups that you’re seeing funded – industries that have a great need, specific applications that are appealing to you as investors.

AI is surely the buzzword right now, again as you mentioned, solving a real world problem can be one of the biggest advantages of AI. Well, it’s a developing trend and the software/hardware ecosystem is complimenting. I am curious to watch how this evolves in coming years.